Learning Deep Structured Models
Authors: Liang-Chieh Chen, Alexander Schwing, Alan Yuille, Raquel Urtasun
ICML 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the effectiveness of our algorithm in the tasks of predicting words from noisy images, as well as tagging of Flickr photographs. We show that joint learning of the deep features and the MRF parameters results in significant performance gains. |
| Researcher Affiliation | Academia | University of California Los Angeles, USA; University of Toronto, 10 King s College Rd., Toronto, Canada |
| Pseudocode | Yes | Figure 1: Algorithm: Deep Structured Learning, Figure 3: Algorithm: Efficient Deep Structured Learning |
| Open Source Code | Yes | The library is released on http://alexander-schwing.de. |
| Open Datasets | Yes | We took the lower case characters from the Chars74K dataset (de Campos et al., 2009)... We initialized the deep-net parameters using a model pre-trained on Image Net (Deng et al., 2009). |
| Dataset Splits | Yes | The training, validation and test sets have 10, 000, 2, 000 and 2, 000 variations of words respectively. For all experiments, the validation set is only used to decrease the step size, i.e., if the accuracy on the validation set decreases, we reduce the step size by 0.5. |
| Hardware Specification | No | The paper states, 'It supports usage of the GPU for the forward and backward pass,' but does not provide specific hardware models (e.g., GPU model, CPU type, or memory size). |
| Software Dependencies | No | The paper mentions C++, HDF5 storage, and Google protocol buffers, but does not provide specific version numbers for any of these software dependencies. |
| Experiment Setup | Yes | In particular, we use a mini-batch size of 100, a step size of 0.01 and a momentum of 0.95. If the unary potential is pre-trained, the initial step size is reduced to 0.001. All the unary classifiers are trained with 100, 000 iterations over mini-batches. We use ϵ = 1, set cr = 1 for all regions r, and perform 10 message passing iterations. |